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Interval Cancer Detection Using a Neural Network and Breast Density in Women with Negative Screening Mammograms
Alexander J T Wanders1, Willem Mees1, Petra A M Bun1
1From the Dutch Breast Cancer Screening Program, Region South-West, Laan 20, 2512 GB, The Hague, the Netherlands (A.J.T.W., W.M., P.A.M.B., C.J.v.R.); Screen-Point Medical, Nijmegen, the Netherlands (N.J., A.R., M.U.D., N.K.); Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands (N.K., I.S., R.M.M.); Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands (C.H.v.G.); Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.); Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.); and Department of Radiology, Haga Teaching Hospital, The Hague, the Netherlands (C.J.v.R.).
Abstract:
Background Inclusion of mammographic breast density (BD) in breast cancer risk models improves accuracy, but accuracy remains modest. Interval cancer (IC) risk prediction may be improved by combining assessments of BD and an artificial intelligence (AI) cancer detection system. Purpose To evaluate the performance of a neural network (NN)-based model that combines the assessments of BD and an AI system in the prediction of risk of developing IC among women with negative screening mammography results. Materials and Methods This retrospective nested case-control study performed with screening examinations included women who developed IC and women with normal follow-up findings (from January 2011 to January 2015). An AI cancer detection system analyzed all studies yielding a score of 1-10, representing increasing likelihood of malignancy. BD was automatically computed using publicly available software. An NN model was trained by combining the AI score and BD using 10-fold cross-validation. Bootstrap analysis was used to calculate the area under the receiver operating characteristic curve (AUC), sensitivity at 90% specificity, and 95% CIs of the AI, BD, and NN models. Results A total of 2222 women with IC and 4661 women in the control group were included (mean age, 61 years; age range, 49-76 years). AUC of the NN model was 0.79 (95% CI: 0.77,0.81), which was higher than AUC of the AI cancer detection system or BD alone (AUC, 0.73 [95% CI: 0.71, 0.76] and 0.69 [95% CI: 0.67, 0.71], respectively; P < .001 for both). At 90% specificity, the NN model had a sensitivity of 50.9% (339 of 666 women; 95% CI: 45.2, 56.3) for prediction of IC, which was higher than that of the AI system (37.5%; 250 of 666 women; 95% CI: 33.0, 43.7; P < .001) or BD percentage alone (22.4%; 149 of 666 women; 95% CI: 17.9, 28.5; P < .001). Conclusion The combined assessment of an artificial intelligence detection system and breast density measurements enabled identification of a larger proportion of women who would develop interval cancer compared with either method alone. Published under a CC BY 4.0 license.
